Although process data indicate that people often rely on sim-plifying processes when choosing between risky options, cur-rent models of heuristics cannot predict people’s choices veryaccurately. To address this apparent paradox, it has been pro-posed that people might adaptively choose from a toolboxof simple strategies. But which strategies are contained inthis toolbox? And how do people decide when to use whichdecision strategy? Here, we develop a model according towhich the decision maker selects a decision strategy for a givenchoice problem rationally from a toolbox of strategies; the con-tent of the toolbox is estimated for each individual decisionmaker. Using cross-validation on an empirical data set, we findthat this model of strategy selection from a personal adaptivetoolbox predicts people’s choices better than any single strat-egy (even when it is allowed to vary across participants) andbetter than previously proposed toolbox models. Our modelcomparisons show that both inferring the content of the tool-box and rational strategy selection are critical for accuratelypredicting people’s risky choices. Furthermore, our analysisreveals considerable individual differences in the set of strate-gies people are equipped with and how they choose amongthem; these individual differences could partly explain whysome people make better choices than others. These findingsrepresent an important step towards a complete formalizationof the notion that people select their cognitive strategies froma personal adaptive toolbox.

People are often confronted with problems whose complexity exceeds their cognitive capacities. To deal with this complexity, individuals and managers can break complex problems down into a series of subgoals. Which subgoals are most effective depends on people's cognitive constraints and the cognitive mechanisms of goal pursuit. This creates an untapped opportunity to derive practical recommendations for which subgoals managers and individuals should set from cognitive models of bounded rationality. To seize this opportunity, we apply the principle of resource rationality to formulate a mathematically precise normative theory of (self-)management by goal-setting. We leverage this theory to computationally derive optimal subgoals from a resource-rational model of human goal pursuit. Finally, we show that the resulting subgoals improve the problem-solving performance of bounded agents and human participants. This constitutes a first step towards grounding prescriptive theories of management and practical recommendations for goal-setting in computational models of the relevant psychological processes and cognitive limitations.